Various types of biometric systems are used more and more in order to provide for increased security and/or enhanced user convenience. In particular, fingerprint sensing systems have been adopted in, for example, consumer electronic devices, thanks to their small form factor, high performance, and user acceptance.
Among the various available fingerprint sensing principles (such as capacitive, optical, thermal etc.), capacitive sensing is most commonly used, in particular in applications where size and power consumption are important issues.
All capacitive fingerprint sensors provide a measure indicative of the capacitance between each of several sensing structures and a finger placed on or moved across the surface of the fingerprint sensor.
Electrical properties (determining analogue signal strengths) of a fingerprint touch sensor depend on external factors. In particular the presence of moist between the finger and the sensor can make a significant difference. Moist can come from the user's finger or be present on the sensor already before the finger is presented. Moist on the finger is typically due to sweating or recent contact with water. Moist on the sensor can also be due to direct contact with water, e.g. after the user has taken a bath or when the device is used in rainy weather, or due to condensation of water from humid air at the sensor surface. In the latter case the amount of condensed water is further dependent on air pressure, temperature, and temperature difference between the air and the sensor surface.
Electromagnetic fields may also affect the electrical properties of the sensor. An electromagnetic field interacting with the sensor circuitry may induce power in circuits and connections, resulting in a shift in the change in electrical potential required to reach the analogue finger detect threshold. This may lead to that the fingerprint image is captured too early or too late.
In order to compensate for analog signal strength variations depending on external factors, trigger thresholds and parameters for analogue-to-digital conversion can be calibrated towards the current conditions. This can be done automatically by various calibration methods, but typically requires that no finger is placed on the sensor during the calibration. Furthermore, a calibration is often performed at system startup and thereafter repeated with given time intervals.
However, continuous calibration at given time intervals may lead to an unnecessarily high power consumption when the fingerprint sensor is unused for long periods of time. The power consumption may be decreased by increasing the time between calibrations. However, this is an undesirable approach since increasing the time between calibrations may lead to situations where the sensor is being used just prior to a scheduled calibration event, increasing the risk that the fingerprint sensor is not properly calibrated for the current operating conditions.
Accordingly, there is a need for an improved method of calibrating a fingerprint sensor.